Asymmetric Contrastive Multimodal Learning for Advancing Chemical
Understanding
- URL: http://arxiv.org/abs/2311.06456v2
- Date: Mon, 20 Nov 2023 21:40:55 GMT
- Title: Asymmetric Contrastive Multimodal Learning for Advancing Chemical
Understanding
- Authors: Hao Xu, Yifei Wang, Yunrui Li, Pengyu Hong
- Abstract summary: Asymmetric Contrastive Multimodal Learning (ACML) is a novel approach tailored for molecules.
ACML harnesses the power of effective asymmetric contrastive learning to seamlessly transfer information from various chemical modalities to molecular graph representations.
- Score: 19.90109687430503
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The versatility of multimodal deep learning holds tremendous promise for
advancing scientific research and practical applications. As this field
continues to evolve, the collective power of cross-modal analysis promises to
drive transformative innovations, leading us to new frontiers in chemical
understanding and discovery. Hence, we introduce Asymmetric Contrastive
Multimodal Learning (ACML) as a novel approach tailored for molecules,
showcasing its potential to advance the field of chemistry. ACML harnesses the
power of effective asymmetric contrastive learning to seamlessly transfer
information from various chemical modalities to molecular graph
representations. By combining pre-trained chemical unimodal encoders and a
shallow-designed graph encoder, ACML facilitates the assimilation of
coordinated chemical semantics from different modalities, leading to
comprehensive representation learning with efficient training. This innovative
framework enhances the interpretability of learned representations and bolsters
the expressive power of graph neural networks. Through practical tasks such as
isomer discrimination and uncovering crucial chemical properties for drug
discovery, ACML exhibits its capability to revolutionize chemical research and
applications, providing a deeper understanding of chemical semantics of
different modalities.
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